RESUMEN
Modeling dynamic interactions among network components is crucial to uncovering the evolution mechanisms of complex networks. Recently, spatio-temporal graph learning methods have achieved noteworthy results in characterizing the dynamic changes of inter-node relations (INRs). However, challenges remain: The spatial neighborhood of an INR is underexploited, and the spatio-temporal dependencies in INRs' dynamic changes are overlooked, ignoring the influence of historical states and local information. In addition, the model's explainability has been understudied. To address these issues, we propose an explainable spatio-temporal graph evolution learning (ESTGEL) model to model the dynamic evolution of INRs. Specifically, an edge attention module is proposed to utilize the spatial neighborhood of an INR at multi-level, i.e., a hierarchy of nested subgraphs derived from decomposing the initial node-relation graph. Subsequently, a dynamic relation learning module is proposed to capture the spatio-temporal dependencies of INRs. The INRs are then used as adjacent information to improve the node representation, resulting in comprehensive delineation of dynamic evolution of the network. Finally, the approach is validated with real data on brain development study. Experimental results on dynamic brain networks analysis reveal that brain functional networks transition from dispersed to more convergent and modular structures throughout development. Significant changes are observed in the dynamic functional connectivity (dFC) associated with functions including emotional control, decision-making, and language processing.
RESUMEN
BACKGROUND: Dengue poses a significant public health concern. Secondary dengue infections with different dengue virus (DENV) serotypes have been linked to an increased risk of severe dengue. This study aimed to assess the risk of severe dengue during secondary infection in Taiwan. METHODS: A retrospective cohort study was conducted using Taiwan's National Health Insurance Research Database to identify dengue cases with secondary dengue infection born after 1944 from 2014 to 2015. Ten matched patients with primary infection were selected as controls using propensity score matching for each secondary dengue infection case. The odds ratio (OR) for severe dengue in secondary versus primary infections was calculated using conditional logistic regression. RESULTS: This study included 357 cases with secondary dengue infection and 3570 matched controls. The risk of severe dengue was found to be 7.8% in individuals with secondary infection, compared to 3.8% in those with primary dengue infection. Secondary infection significantly increased the risk of severe dengue (OR 2.13, 95% CI: 1.40-3.25, P = 0.0004). Notably, a significant association between secondary infection and severe dengue was observed only when the interval between the first and secondary infection was greater than two years (OR 3.19, 95% CI 2.04-5.00, P < 0.0001). CONCLUSION: Secondary dengue infection significantly increases the risk of severe disease in Taiwan, particularly when the interval between infections is over two years. Healthcare professionals should maintain heightened vigilance for individuals with a history of previous dengue infection, particularly if their initial diagnosis was more than two years prior.
RESUMEN
Testosterone levels sharply rise during the transition from childhood to adolescence and these changes are known to be associated with changes in human brain structure. During this same developmental window, there are also robust changes in the neural oscillatory dynamics serving verbal working memory processing. Surprisingly, whereas many studies have investigated the effects of chronological age on the neural oscillations supporting verbal working memory, none have probed the impact of endogenous testosterone levels during this developmental period. Using a sample of 89 youth aged 6-14 years-old, we collected salivary testosterone samples and recorded magnetoencephalography during a modified Sternberg verbal working memory task. Significant oscillatory responses were identified and imaged using a beamforming approach and the resulting maps were subjected to whole-brain ANCOVAs examining the effects of testosterone and sex, controlling for age, during verbal working memory encoding and maintenance. Our primary results indicated robust testosterone-related effects in theta (4-7 Hz) and alpha (8-14 Hz) oscillatory activity, controlling for age. During encoding, females exhibited weaker theta oscillations than males in right cerebellar cortices and stronger alpha oscillations in left temporal cortices. During maintenance, youth with greater testosterone exhibited weaker alpha oscillations in right parahippocampal and cerebellar cortices, as well as regions across the left-lateralized language network. These results extend the existing literature on the development of verbal working memory processing by showing region and sex-specific effects of testosterone, and are the first results to link endogenous testosterone levels to the neural oscillatory activity serving verbal working memory, above and beyond the effects of chronological age.
Asunto(s)
Magnetoencefalografía , Memoria a Corto Plazo , Testosterona , Humanos , Masculino , Memoria a Corto Plazo/fisiología , Femenino , Adolescente , Niño , Encéfalo/fisiología , Saliva/química , Saliva/metabolismo , Mapeo Encefálico , Caracteres SexualesAsunto(s)
Glucósidos Iridoides , Daño por Reperfusión Miocárdica , Proteínas Proto-Oncogénicas c-akt , Factor de Transcripción STAT3 , Factor de Transcripción STAT3/metabolismo , Daño por Reperfusión Miocárdica/prevención & control , Daño por Reperfusión Miocárdica/metabolismo , Animales , Proteínas Proto-Oncogénicas c-akt/metabolismo , Glucósidos Iridoides/farmacología , Glucósidos Iridoides/uso terapéutico , Transducción de Señal/efectos de los fármacos , Transducción de Señal/fisiología , Masculino , RatasRESUMEN
OBJECTIVE: Brain dynamic effective connectivity (dEC), characterizes the information transmission patterns between brain regions that change over time, which provides insight into the biological mechanism underlying brain development. However, most existing methods predominantly capture fixed or temporally invariant EC, leaving dEC largely unexplored. METHODS: Herein we propose a deep dynamic causal learning model specifically designed to capture dEC. It includes a dynamic causal learner to detect time-varying causal relationships from spatio-temporal data, and a dynamic causal discriminator to validate these findings by comparing original and reconstructed data. RESULTS: Our model outperforms established baselines in the accuracy of identifying dynamic causalities when tested on the simulated data. When applied to the Philadelphia Neurodevelopmental Cohort, the model uncovers distinct patterns in dEC networks across different age groups. Specifically, the evolution process of brain dEC networks in young adults is more stable than in children, and significant differences in information transfer patterns exist between them. CONCLUSION: This study highlights the brain's developmental trajectory, where networks transition from undifferentiated to specialized structures with age, in accordance with the improvement of an individual's cognitive and information processing capability. SIGNIFICANCE: The proposed model consists of the identification and verification of dynamic causality, utilizing the spatio-temporal fusing information from fMRI. As a result, it can accurately detect dEC and characterize its evolution over age.
RESUMEN
BACKGROUND: Dengue virus (DENV) infection, a common mosquito-borne disease, has been linked to several mental disorders like depression and anxiety. However, the temporal risk of these disorders after DENV infection is not well studied. METHODS: This population-based cohort study encompassed 45,334 recently lab-confirmed dengue patients in Taiwan spanning 2002 to 2015, matched at a 1:5 ratio with non-dengue individuals based on age, gender, and residence (n = 226,670). Employing subdistribution hazard regression analysis, we assessed the immediate (<3 months), intermediate (3-12 months), and prolonged (>12 months) risks of anxiety disorders, depressive disorders, and sleep disorders post DENV infection. Corrections for multiple comparisons were carried out using the Benjamini-Hochberg procedure. RESULTS: A significant increase in depressive disorder risk across all timeframes post-infection was observed (<3 months [aSHR 1.90, 95% CI 1.20-2.99], 3-12 months [aSHR 1.68, 95% CI 1.32-2.14], and >12 months [aSHR 1.14, 95% CI 1.03-1.25]). Sleep disorder risk was higher only during 3-12 months (aSHR 1.55, 95% CI 1.18-2.04). No elevated anxiety disorder risk was found. Subgroup analysis of hospitalized dengue patients showed increased risk of anxiety disorders within 3 months (aSHR 2.14, 95% CI 1.19-3.85) and persistent risk of depressive disorders across all periods. Hospitalized dengue patients also had elevated sleep disorder risk within the first year. CONCLUSION: Dengue patients exhibited significantly elevated risks of depressive disorders in both the short and long term. However, dengue's impact on sleep disorders and anxiety seems to be short-lived. Further research is essential to elucidate the underlying mechanisms.
Asunto(s)
Trastornos de Ansiedad , Dengue , Trastorno Depresivo , Trastornos del Sueño-Vigilia , Humanos , Dengue/epidemiología , Dengue/complicaciones , Masculino , Femenino , Taiwán/epidemiología , Trastornos del Sueño-Vigilia/epidemiología , Adulto , Trastornos de Ansiedad/epidemiología , Estudios de Cohortes , Adulto Joven , Persona de Mediana Edad , Adolescente , Trastorno Depresivo/epidemiología , Factores de Riesgo , Niño , Anciano , PreescolarRESUMEN
Grain weight, a key determinant of yield in rice (Oryza sativa L.), is governed primarily by genetic factors, whereas grain chalkiness, a detriment to grain quality, is intertwined with environmental factors such as mineral nutrients. Nitrogen (N) is recognized for its effect on grain chalkiness, but the underlying molecular mechanisms remain to be clarified. This study revealed the pivotal role of rice NODULE INCEPTION-LIKE PROTEIN 3 (OsNLP3) in simultaneously regulating grain weight and grain chalkiness. Our investigation showed that loss of OsNLP3 leads to a reduction in both grain weight and dimension, in contrast to the enhancement observed with OsNLP3 overexpression. OsNLP3 directly suppresses the expression of OsCEP6.1 and OsNF-YA8, which were identified as negative regulators associated with grain weight. Consequently, two novel regulatory modules, OsNLP3-OsCEP6.1 and OsNLP3-OsNF-YA8, were identified as key players in grain weight regulation. Notably, the OsNLP3-OsNF-YA8 module not only increases grain weight but also mitigates grain chalkiness in response to N. This research clarifies the molecular mechanisms that orchestrate grain weight through the OsNLP3-OsCEP6.1 and OsNLP3-OsNF-YA8 modules, highlighting the pivotal role of the OsNLP3-OsNF-YA8 module in alleviating grain chalkiness. These findings reveal potential targets for simultaneous enhancement of rice yield and quality.
RESUMEN
PURPOSE: To explore the utility of high frequency oscillations (HFO) and long-range temporal correlations (LRTCs) in preoperative assessment of epilepsy. METHODS: MEG ripples were detected in 59 drug-resistant epilepsy patients, comprising 5 with parietal lobe epilepsy (PLE), 21 with frontal lobe epilepsy (FLE), 14 with lateral temporal lobe epilepsy (LTLE), and 19 with mesial temporal lobe epilepsy (MTLE) to identify the epileptogenic zone (EZ). The results were compared with clinical MEG reports and resection area. Subsequently, LRTCs were quantified at the source-level by detrended fluctuation analysis (DFA) and life/waiting -time at 5 bands for 90 cerebral cortex regions. The brain regions with larger DFA exponents and standardized life-waiting biomarkers were compared with the resection results. RESULTS: Compared to MEG sensor-level data, ripple sources were more frequently localized within the resection area. Moreover, source-level analysis revealed a higher proportion of DFA exponents and life-waiting biomarkers with relatively higher rankings, primarily distributed within the resection area (p<0.01). Moreover, these two LRCT indices across five distinct frequency bands correlated with EZ. CONCLUSION: HFO and source-level LRTCs are correlated with EZ. Integrating HFO and LRTCs may be an effective approach for presurgical evaluation of epilepsy.
Asunto(s)
Epilepsia Refractaria , Epilepsias Parciales , Magnetoencefalografía , Humanos , Magnetoencefalografía/métodos , Femenino , Adulto , Masculino , Epilepsias Parciales/cirugía , Epilepsias Parciales/fisiopatología , Adulto Joven , Epilepsia Refractaria/cirugía , Epilepsia Refractaria/fisiopatología , Adolescente , Persona de Mediana Edad , Electroencefalografía/métodos , Corteza Cerebral/fisiopatología , Corteza Cerebral/cirugía , Cuidados Preoperatorios/métodos , Ondas Encefálicas/fisiologíaRESUMEN
Aims: The population pharmacokinetic (PPK) model-based machine learning (ML) approach offers a novel perspective on individual concentration prediction. This study aimed to establish a PPK-based ML model for predicting tacrolimus (TAC) concentrations in Chinese renal transplant recipients. Methods: Conventional TAC monitoring data from 127 Chinese renal transplant patients were divided into training (80%) and testing (20%) datasets. A PPK model was developed using the training group data. ML models were then established based on individual pharmacokinetic data derived from the PPK basic model. The prediction performances of the PPK-based ML model and Bayesian forecasting approach were compared using data from the test group. Results: The final PPK model, incorporating hematocrit and CYP3A5 genotypes as covariates, was successfully established. Individual predictions of TAC using the PPK basic model, postoperative date, CYP3A5 genotype, and hematocrit showed improved rankings in ML model construction. XGBoost, based on the TAC PPK, exhibited the best prediction performance. Conclusion: The PPK-based machine learning approach emerges as a superior option for predicting TAC concentrations in Chinese renal transplant recipients.
RESUMEN
The pituitary gland (PG) plays a central role in the production and secretion of pubertal hormones, with documented links to the emergence and increase in mental health symptoms known to occur during adolescence. Although much of the literature has focused on examining whole PG volume, recent findings suggest that there are associations among pubertal hormone levels, including dehydroepiandrosterone (DHEA), subregions of the PG, and elevated mental health symptoms (e.g., internalizing symptoms) during adolescence. Surprisingly, studies have not yet examined associations among these factors and increasing transdiagnostic symptomology, despite DHEA being a primary output of the anterior PG. Therefore, the current study sought to fill this gap by examining whether anterior PG volume specifically mediates associations between DHEA levels and changes in dysregulation symptoms in an adolescent sample ( N = 114, 9 - 17 years, M age = 12.87, SD = 1.88). Following manual tracing of the anterior and posterior PG, structural equation modeling revealed that greater anterior, not posterior, PG volume mediated the association between greater DHEA levels and increasing dysregulation symptoms across time, controlling for baseline dysregulation symptom levels. These results suggest specificity in the role of the anterior PG in adrenarcheal processes that may confer risk for psychopathology during adolescence. This work not only highlights the importance of separately tracing the anterior and posterior PG, but also suggests that transdiagnostic factors like dysregulation are useful in parsing hormone-related increases in mental health symptoms in youth.
RESUMEN
Objective: fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging. Methods: We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevel-opmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP). Results: We find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity. Conclusion: Our model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics. Significance: Our DemoVAE model allows for generation of high quality synthetic data conditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds.
RESUMEN
INTRODUCTION: The human cerebellum emerges as a posterior brain structure integrating neural networks for sensorimotor, cognitive, and emotional processing across the lifespan. Developmental studies of the cerebellar anatomy and function are scant. We examine age-dependent MRI morphometry of the anterior cerebellar vermis, lobules I-V and posterior neocortical lobules VI-VII and their relationship to sensorimotor and cognitive functions. METHODS: Typically developing children (TDC; n=38; age 9-15) and healthy adults (HAC; n=31; 18-40) participated in high-resolution MRI. Rigorous anatomically informed morphometry of the vermis lobules I-V and VI-VII and total brain volume (TBV) employed manual segmentation computer-assisted FreeSurfer Image Analysis Program [http://surfer.nmr.mgh.harvard.edu]. The neuropsychological scores (WASI-II) were normalized and related to volumes of anterior, posterior vermis, and TBV. RESULTS: TBVs were age independent. Volumes of I-V and VI-VII were significantly reduced in TDC. The ratio of VI-VII to I-V (â¼60%) was stable across age-groups; I-V correlated with visual-spatial-motor skills; VI-VII with verbal, visual-abstract and FSIQ. CONCLUSIONS: In TDC neither anterior I-V nor posterior VI-VII vermis attained adult volumes. The "inverted U" developmental trajectory of gray matter peaking in adolescence does not explain this finding. The hypothesis of protracted development of oligodendrocyte/myelination is suggested as a contributor to TDC's lower cerebellar vermis volumes.
Asunto(s)
Vermis Cerebeloso , Cognición , Imagen por Resonancia Magnética , Humanos , Adolescente , Niño , Femenino , Masculino , Imagen por Resonancia Magnética/métodos , Cognición/fisiología , Adulto , Adulto Joven , Vermis Cerebeloso/diagnóstico por imagen , Cerebelo/diagnóstico por imagen , Cerebelo/anatomía & histologíaRESUMEN
Objective: fMRI and derived measures such as functional connectivity (FC) have been used to predict brain age, general fluid intelligence, psychiatric disease status, and preclinical neurodegenerative disease. However, it is not always clear that all demographic confounds, such as age, sex, and race, have been removed from fMRI data. Additionally, many fMRI datasets are restricted to authorized researchers, making dissemination of these valuable data sources challenging. Methods: We create a variational autoencoder (VAE)-based model, DemoVAE, to decorrelate fMRI features from demographics and generate high-quality synthetic fMRI data based on user-supplied demographics. We train and validate our model using two large, widely used datasets, the Philadelphia Neurodevelopmental Cohort (PNC) and Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP). Results: We find that DemoVAE recapitulates group differences in fMRI data while capturing the full breadth of individual variations. Significantly, we also find that most clinical and computerized battery fields that are correlated with fMRI data are not correlated with DemoVAE latents. An exception are several fields related to schizophrenia medication and symptom severity. Conclusion: Our model generates fMRI data that captures the full distribution of FC better than traditional VAE or GAN models. We also find that most prediction using fMRI data is dependent on correlation with, and prediction of, demographics. Significance: Our DemoVAE model allows for generation of high quality synthetic data conditioned on subject demographics as well as the removal of the confounding effects of demographics. We identify that FC-based prediction tasks are highly influenced by demographic confounds.
RESUMEN
Throughout childhood and adolescence, the brain undergoes significant structural and functional changes that contribute to the maturation of multiple cognitive domains, including selective attention. Selective attention is crucial for healthy executive functioning and while key brain regions serving selective attention have been identified, their age-related changes in neural oscillatory dynamics and connectivity remain largely unknown. We examined the developmental sensitivity of selective attention circuitry in 91 typically developing youth aged 6 - 13 years old. Participants completed a number-based Simon task while undergoing magnetoencephalography (MEG) and the resulting data were preprocessed and transformed into the time-frequency domain. Significant oscillatory brain responses were imaged using a beamforming approach, and task-related peak voxels in the occipital, parietal, and cerebellar cortices were used as seeds for subsequent whole-brain connectivity analyses in the alpha and gamma range. Our key findings revealed developmentally sensitive connectivity profiles in multiple regions crucial for selective attention, including the temporoparietal junction (alpha) and prefrontal cortex (gamma). Overall, these findings suggest that brain regions serving selective attention are highly sensitive to developmental changes during the pubertal transition period.
RESUMEN
Some robust point cloud registration approaches with controllable pose refinement magnitude, such as ICP and its variants, are commonly used to improve 6D pose estimation accuracy. However, the effectiveness of these methods gradually diminishes with the advancement of deep learning techniques and the enhancement of initial pose accuracy, primarily due to their lack of specific design for pose refinement. In this paper, we propose Point Cloud Completion and Keypoint Refinement with Fusion Data (PCKRF), a new pose refinement pipeline for 6D pose estimation. The pipeline consists of two steps. First, it completes the input point clouds via a novel pose-sensitive point completion network. The network uses both local and global features with pose information during point completion. Then, it registers the completed object point cloud with the corresponding target point cloud by our proposed Color supported Iterative KeyPoint (CIKP) method. The CIKP method introduces color information into registration and registers a point cloud around each keypoint to increase stability. The PCKRF pipeline can be integrated with existing popular 6D pose estimation methods, such as the full flow bidirectional fusion network, to further improve their pose estimation accuracy. Experiments demonstrate that our method exhibits superior stability compared to existing approaches when optimizing initial poses with relatively high precision. Notably, the results indicate that our method effectively complements most existing pose estimation techniques, leading to improved performance in most cases. Furthermore, our method achieves promising results even in challenging scenarios involving textureless and symmetrical objects. Our source code is available at https://github.com/zhanhz/KRF.
RESUMEN
Purpose: Functional magnetic resonance imaging (fMRI) and functional connectivity (FC) have been used to follow aging in both children and older adults. Robust changes have been observed in children, in which high connectivity among all brain regions changes to a more modular structure with maturation. We examine FC changes in older adults after 2 years of aging in the UK Biobank (UKB) longitudinal cohort. Approach: We process fMRI connectivity data using the Power264 atlas and then test whether the average internetwork FC changes in the 2722-subject longitudinal cohort are statistically significant using a Bonferroni-corrected t-test. We also compare the ability of Power264 and UKB-provided, independent component analysis (ICA)-based FC to determine which of a longitudinal scan pair is older. Finally, we investigate cross-sectional FC changes as well as differences due to differing scanner tasks in the UKB, Philadelphia Neurodevelopmental Cohort, and Alzheimer's Disease Neuroimaging Initiative datasets. Results: We find a 6.8% average increase in somatomotor network (SMT)-visual network (VIS) connectivity from younger to older scans (corrected p<10-15) that occurs in male, female, older subject (>65 years old), and younger subject (<55 years old) groups. Among all internetwork connections, the average SMT-VIS connectivity is the best predictor of relative scan age. Using the full FC and a training set of 2000 subjects, one is able to predict which scan is older 82.5% of the time using either the full Power264 FC or the UKB-provided ICA-based FC. Conclusions: We conclude that SMT-VIS connectivity increases with age in the UKB longitudinal cohort and that resting state FC increases with age in the UKB cross-sectional cohort.
Asunto(s)
Músculo Liso Vascular , Miocitos del Músculo Liso , Fenotipo , Sirtuinas , Músculo Liso Vascular/metabolismo , Sirtuinas/metabolismo , Humanos , Miocitos del Músculo Liso/metabolismo , Miocitos del Músculo Liso/fisiología , Proteínas Serina-Treonina Quinasas/metabolismo , Células Cultivadas , Animales , Quinasas de la Proteína-Quinasa Activada por el AMP , Proteínas Quinasas Activadas por AMP/metabolismo , RatonesRESUMEN
Heavy metal migration in soil poses a serious threat to the soil and groundwater. Understanding the migration pattern of heavy metals (HMs) under different factors could provide a more reasonable position for pollution evaluation and targetoriented treatment of soil heavy metal. In this study, the migration behavior of Pb and Cd in co-contaminated soil under different pH and ionic strength (NaCl concentration) was simulated using convective dispersion equation (CDE). We predicted the migration trends of Pb and Cd in soils after 5, 10, and 20 years via PHREEQC. The results showed that the migration time of Cd in the soil column experiment was about 60 days faster than that of Pb, and the migration trend was much steeper. The CDE was proved to describe the migration behavior of Pb and Cd (R2 > 0.75) in soil. The predicted results showed that Cd migrated to 15-20 cm of soil within 7 years and Pb stayed mainly in the top 0-6 cm of soil within 5 years as the duration of irrigation increased. Overall, our study is expected to provide new insight into the migration of heavy metal in soil ecosystems and guidance for reducing risk of heavy metal in the environment.
RESUMEN
INTRODUCTION: Gestational diabetes mellitus (GDM) is the most common metabolic disease in pregnancy. However, studies of activating molecule of Beclin1-regulated autophagy (Ambra1) affecting the insulin substrate receptor 1/phosphatidylinositol 3 kinase/protein kinase B (IRS-1/PI3K/Akt) signalling pathway in GDM have not been reported. The aim of the study was to detect the difference of Ambra1 expression in the placenta of normal pregnant women and GDM patients. MATERIAL AND METHODS: An in vitro model of gestational diabetes mellitus was established by inducing HTR8/Svneo cells from human chorionic trophoblast layer with high glucose. The changes of cell morphology were observed by inverted microscope, and the expression levels of Ambra1 gene and protein in model cells were detected. After this, Ambra1 gene was silenced by shRNA transfection, and PI3K inhibitor was added to detect changes in Ambra1, autophagy, and insulin (INS) signalling pathways. RESULTS: The protein expression levels of Ambra1, Bcl-2 interacting protein (Beclin-1), and microtubule-associated proteins 1A/1B light chain 3B (LC3-II) in the placentas of GDM pregnant women were higher than those of normal pregnant women. High glucose induces morphological changes in HTR8/Svneo cells and increases Ambra1 transcription and translation levels. sh-Ambra1 increased survival of HTR8/SvNEO-HG cells and inhibited Ambra1, Beclin1, and LC3-II transcription and translation levels. Also, sh-Ambra1 increased IRS-1/PI3K/Akt protein phosphorylation levels and inhibited the IRS-1/PI3K/Akt signalling pathway and its resulting autophagy. CONCLUSIONS: sh-Ambra1 increased IRS-1/PI3K/Akt protein phosphorylation levels to reduce autophagy in gestational diabetes.
Asunto(s)
Diabetes Gestacional , Femenino , Humanos , Embarazo , Autofagia , Beclina-1 , Diabetes Gestacional/metabolismo , Glucosa/metabolismo , Insulina/metabolismo , Fosfatidilinositol 3-Quinasas/metabolismo , Proteínas Proto-Oncogénicas c-akt/metabolismoRESUMEN
Recently, functional magnetic resonance imaging (fMRI) based functional connectivity network (FCN) analysis via graph convolutional networks (GCNs) has shown promise for automated diagnosis of brain diseases by regarding the FCNs as irregular graph-structured data. However, multiview information and site influences of the FCNs in a multisite, multiatlas fMRI scenario have been understudied. In this paper, we propose a Class-consistency and Site-independence Multiview Hyperedge-Aware HyperGraph Embedding Learning (CcSi-MHAHGEL) framework to integrate FCNs constructed on multiple brain atlases in a multisite fMRI study. Specifically, for each subject, we first model brain network as a hypergraph for every brain atlas to characterize high-order relations among multiple vertexes, and then introduce a multiview hyperedge-aware hypergraph convolutional network (HGCN) to extract a multiatlas-based FCN embedding where hyperedge weights are adaptively learned rather than employing the fixed weights precalculated in traditional HGCNs. In addition, we formulate two modules to jointly learn the multiatlas-based FCN embeddings by considering the between-subject associations across classes and sites, respectively, i.e., a class-consistency module to encourage both compactness within every class and separation between classes for promoting discrimination in the embedding space, and a site-independence module to minimize the site dependence of the embeddings for mitigating undesired site influences due to differences in scanning platforms and/or protocols at multiple sites. Finally, the multiatlas-based FCN embeddings are fed into a few fully connected layers followed by the soft-max classifier for diagnosis decision. Extensive experiments on the ABIDE demonstrate the effectiveness of our method for autism spectrum disorder (ASD) identification. Furthermore, our method is interpretable by revealing ASD-relevant brain regions that are biologically significant.